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Unsupervised dense crowd detection by multiscale texture analysis

Abstract : This study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised.
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Contributor : Nicolas Courty Connect in order to contact the contributor
Submitted on : Thursday, November 14, 2013 - 9:43:29 AM
Last modification on : Thursday, April 21, 2022 - 3:36:02 PM
Long-term archiving on: : Saturday, February 15, 2014 - 4:30:32 AM


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  • HAL Id : hal-00904210, version 1


Fagette Antoine, Nicolas Courty, Daniel Racoceanu, Jean-Yves Dufour. Unsupervised dense crowd detection by multiscale texture analysis. Pattern Recognition Letters, Elsevier, 2013, pp.1-27. ⟨hal-00904210⟩



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